Comparison of the Completeness of Prescription Medication Histories for Hospitalized Geriatric Patients Documented by Different Health Care Professionals
Bibliographic record
Abstract
ABSTRACT Objective: The primary objective of this study was to evaluate the completeness of medication-history information for hospitalized geriatric patients documented by various health care professionals. A secondary objective was to assess the utility of the computerized prescription database PharmaNet for identifying medications taken before admission to hospital and drug-related problems related to these medications. Methods: A retrospective review of 55 charts from patients over age 65 years admitted to hospital between November 1999 and February 2000 who had taken at least one prescription medication before admission to hospital. Results: Pharmacists tended to identify a higher proportion of medications taken before admission than other professionals (76% ± 25% versus 70% ± 29%, p = 0.25). Pharmacists’ use of PharmaNet did not seem to affect the proportion of identified medications taken before admission (76% ± 21% versus 77% ± 30%, p = 0.94); however, patients for whom PharmaNet was used had taken more medications before admission (7.0 ± 2.8 versus 5.4 ± 2.8, p = 0.10). More drug-related problems per patient were documented when PharmaNet was used (1.5 ± 1.3) than when PharmaNet was not used (0.6 ± 0.9) (p = 0.02), whereas the mean severity index of drug-related problems was similar (1.4 ± 0.6 versus 1.2 ± 0.4). The most common category of drug-related problems documented with PharmaNet use was an untreated indication. Conclusions: Pharmacists may document the most comprehensive medication-history information for geriatric patients, but the use of PharmaNet did not seem to significantly enhance the completeness of the information obtained. More drug-related problems were documented when PharmaNet was reviewed, but it was unclear whether this was attributable to the use of PharmaNet. Although prescription databases are a convenient source of medication-history information, pharmacists may also need to talk to patients directly to obtain complete data. A prospective, randomized study using a definitive assessment of medications taken before admission as a comparator is required before firm conclusions can be drawn. RESUME Objectif : Le principal objectif de cette etude etait d’evaluer l’exhaustivite de la documentation, par les divers professionnels de la sante, de l’histoire medicamenteuse des patients âges hospitalises. L’objectif secondaire etait d’evaluer l’utilite de la base de donnees informatique sur les ordonnances, PharmaNet, pour depister les medicaments que prenaient ces patients avant leur hospitalisation ainsi que les problemes pharmacotherapeutiques lies a ces medicaments. Methodes : Une analyse retrospective de 55 dossiers de patients âges de plus de 65 ans, qui avaient ete admis a l’hopital entre novembre 1999 et fevrier 2000, et qui avaient pris au moins un medicament d’ordonnance avant leur hospitalisation a ete menee. Resultats : Les pharmaciens avaient tendance a identifier un plus grand nombre de medicaments pris avant l’admission, que les autres professionnels de la sante (76 % ± 25 % vs 70 % ± 29 %, p = 0,25). L’utilisation de PharmaNet par les pharmaciens n’a cependant pas semble avoir d’effet sur la proportion des medicaments pre-admission identifies (76 % ± 21 % vs 77 % ± 30 %, p = 0,94). En revanche, les patients pour lesquels les pharmaciens ont eu recours a PharmaNet prenaient un plus grand nombre de medicaments avant leur admission (7,0 ± 2,8 vs 5,4 ± 2,8, p = 0,10). Par ailleurs, le nombre de problemes relies a la pharmacotherapie documentes lorsque PharmaNet etait utilise etait de 1,5 ± 1,3 par patient, comparativement a 0,6 ± 0,9 lorsque PharmaNet n’etait pas utilise (p = 0,02), alors que l’indice de gravite moyen de ces problemes etait semblable (1,4 ± 0,6 vs 1,2 ± 0,4) dans un cas comme dans l’autre. La categorie de problemes relies a la pharmacotherapie la plus frequemment documentee avec l’utilisation de PharmaNet etait l’absence de traitement pour une indication valide. Conclusions : Les pharmaciens documentent peut-etre de facon plus exhaustive l’histoire medicamenteuse des patients âges, mais l’utilisation de PharmaNet n’a pas semble accroitre significativement l’exhaustivite de l’information obtenue. Toutefois, le recours a PharmaNet a permis de documenter un plus grand nombre de problemes relies a la pharmacotherapie, bien qu’on ne puisse dire clairement si cela etait attribuable a son utilisation. Malgre que les bases de donnees sur les ordonnances constituent une source pratique d’information sur l’histoire medicamenteuse, les pharmaciens pourraient devoir s’adresser aux patients directement afin d’obtenir des renseignements complets. Une etude prospective et randomisee, faisant appel a une evaluation definitive des medicaments pris avant l’admission des patients a titre de groupe temoin, est necessaire avant de pouvoir tirer des conclusions certaines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".